Datasets:
KaiLv
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UDR_Java

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public static StringBuilder appendSpace ( StringBuilder buf , int spaces ) { for ( int i = spaces ; i > 0 ; i -= SPACEPADDING . length ) { buf . append ( SPACEPADDING , 0 , i < SPACEPADDING . length ? i : SPACEPADDING . length ) ; } return buf ; }
Append whitespace to a buffer .
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protected void makeLayerElement ( ) { plotwidth = StyleLibrary . SCALE ; plotheight = StyleLibrary . SCALE / optics . getOPTICSPlot ( context ) . getRatio ( ) ; final double margin = context . getStyleLibrary ( ) . getSize ( StyleLibrary . MARGIN ) ; layer = SVGUtil . svgElement ( svgp . getDocument ( ) , SVGConstants . SVG_G_TAG ) ; final String transform = SVGUtil . makeMarginTransform ( getWidth ( ) , getHeight ( ) , plotwidth , plotheight , margin * .5 , margin * .5 , margin * 1.5 , margin * .5 ) ; SVGUtil . setAtt ( layer , SVGConstants . SVG_TRANSFORM_ATTRIBUTE , transform ) ; }
Produce a new layer element .
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protected List < Centroid > computeCentroids ( int dim , List < V > vectorcolumn , List < ClassLabel > keys , Map < ClassLabel , IntList > classes ) { final int numc = keys . size ( ) ; List < Centroid > centroids = new ArrayList <> ( numc ) ; for ( int i = 0 ; i < numc ; i ++ ) { Centroid c = new Centroid ( dim ) ; for ( IntIterator it = classes . get ( keys . get ( i ) ) . iterator ( ) ; it . hasNext ( ) ; ) { c . put ( vectorcolumn . get ( it . nextInt ( ) ) ) ; } centroids . add ( c ) ; } return centroids ; }
Compute the centroid for each class .
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protected double kNNDistance ( ) { double knnDist = 0. ; for ( int i = 0 ; i < getNumEntries ( ) ; i ++ ) { MkMaxEntry entry = getEntry ( i ) ; knnDist = Math . max ( knnDist , entry . getKnnDistance ( ) ) ; } return knnDist ; }
Determines and returns the k - nearest neighbor distance of this node as the maximum of the k - nearest neighbor distances of all entries .
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@ Override public boolean adjustEntry ( MkMaxEntry entry , DBID routingObjectID , double parentDistance , AbstractMTree < O , MkMaxTreeNode < O > , MkMaxEntry , ? > mTree ) { super . adjustEntry ( entry , routingObjectID , parentDistance , mTree ) ; // adjust knn distance entry . setKnnDistance ( kNNDistance ( ) ) ; return true ; // TODO: improve }
Calls the super method and adjust additionally the k - nearest neighbor distance of this node as the maximum of the k - nearest neighbor distances of all its entries .
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@ Override protected void integrityCheckParameters ( MkMaxEntry parentEntry , MkMaxTreeNode < O > parent , int index , AbstractMTree < O , MkMaxTreeNode < O > , MkMaxEntry , ? > mTree ) { super . integrityCheckParameters ( parentEntry , parent , index , mTree ) ; // test if knn distance is correctly set MkMaxEntry entry = parent . getEntry ( index ) ; double knnDistance = kNNDistance ( ) ; if ( Math . abs ( entry . getKnnDistance ( ) - knnDistance ) > 0 ) { throw new RuntimeException ( "Wrong knnDistance in node " + parent . getPageID ( ) + " at index " + index + " (child " + entry + ")" + "\nsoll: " + knnDistance + ",\n ist: " + entry . getKnnDistance ( ) ) ; } }
Calls the super method and tests if the k - nearest neighbor distance of this node is correctly set .
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@ Override public void initialize ( ) { if ( databaseConnection == null ) { return ; // Supposedly we initialized already. } if ( LOG . isDebugging ( ) ) { LOG . debugFine ( "Loading data from database connection." ) ; } MultipleObjectsBundle bundle = databaseConnection . loadData ( ) ; // Run at most once. databaseConnection = null ; // Find DBIDs for bundle { DBIDs bids = bundle . getDBIDs ( ) ; if ( bids instanceof ArrayStaticDBIDs ) { this . ids = ( ArrayStaticDBIDs ) bids ; } else if ( bids == null ) { this . ids = DBIDUtil . generateStaticDBIDRange ( bundle . dataLength ( ) ) ; } else { this . ids = ( ArrayStaticDBIDs ) DBIDUtil . makeUnmodifiable ( DBIDUtil . ensureArray ( bids ) ) ; } } // Replace id representation (it would be nicer if we would not need // DBIDView at all) this . idrep = new DBIDView ( this . ids ) ; relations . add ( this . idrep ) ; getHierarchy ( ) . add ( this , idrep ) ; DBIDArrayIter it = this . ids . iter ( ) ; int numrel = bundle . metaLength ( ) ; for ( int i = 0 ; i < numrel ; i ++ ) { SimpleTypeInformation < ? > meta = bundle . meta ( i ) ; @ SuppressWarnings ( "unchecked" ) SimpleTypeInformation < Object > ometa = ( SimpleTypeInformation < Object > ) meta ; WritableDataStore < Object > store = DataStoreUtil . makeStorage ( ids , DataStoreFactory . HINT_DB , ometa . getRestrictionClass ( ) ) ; for ( it . seek ( 0 ) ; it . valid ( ) ; it . advance ( ) ) { store . put ( it , bundle . data ( it . getOffset ( ) , i ) ) ; } Relation < ? > relation = new MaterializedRelation <> ( ometa , ids , null , store ) ; relations . add ( relation ) ; getHierarchy ( ) . add ( this , relation ) ; // Try to add indexes where appropriate for ( IndexFactory < ? > factory : indexFactories ) { if ( factory . getInputTypeRestriction ( ) . isAssignableFromType ( ometa ) ) { @ SuppressWarnings ( "unchecked" ) final IndexFactory < Object > ofact = ( IndexFactory < Object > ) factory ; @ SuppressWarnings ( "unchecked" ) final Relation < Object > orep = ( Relation < Object > ) relation ; final Index index = ofact . instantiate ( orep ) ; Duration duration = LOG . isStatistics ( ) ? LOG . newDuration ( index . getClass ( ) . getName ( ) + ".construction" ) . begin ( ) : null ; index . initialize ( ) ; if ( duration != null ) { LOG . statistics ( duration . end ( ) ) ; } getHierarchy ( ) . add ( relation , index ) ; } } } // fire insertion event eventManager . fireObjectsInserted ( ids ) ; }
Initialize the database by getting the initial data from the database connection .
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protected void zSort ( List < ? extends SpatialComparable > objs , int start , int end , double [ ] mms , int [ ] dims , int depth ) { final int numdim = ( dims != null ) ? dims . length : ( mms . length >> 1 ) ; final int edim = ( dims != null ) ? dims [ depth ] : depth ; // Find the splitting points. final double min = mms [ 2 * edim ] , max = mms [ 2 * edim + 1 ] ; double spos = ( min + max ) / 2. ; // Safeguard against duplicate points: if ( max - spos < STOPVAL || spos - min < STOPVAL ) { boolean ok = false ; for ( int d = 0 ; d < numdim ; d ++ ) { int d2 = ( ( dims != null ) ? dims [ d ] : d ) << 1 ; if ( mms [ d2 + 1 ] - mms [ d2 ] >= STOPVAL ) { ok = true ; break ; } } if ( ! ok ) { return ; } } int split = pivotizeList1D ( objs , start , end , edim , spos , false ) ; assert ( start <= split && split <= end ) ; int nextdim = ( depth + 1 ) % numdim ; if ( start < split - 1 ) { mms [ 2 * edim ] = min ; mms [ 2 * edim + 1 ] = spos ; zSort ( objs , start , split , mms , dims , nextdim ) ; } if ( split < end - 1 ) { mms [ 2 * edim ] = spos ; mms [ 2 * edim + 1 ] = max ; zSort ( objs , split , end , mms , dims , nextdim ) ; } // Restore ranges mms [ 2 * edim ] = min ; mms [ 2 * edim + 1 ] = max ; }
The actual Z sorting function
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public StringBuilder appendTo ( StringBuilder buf ) { return buf . append ( DBIDUtil . toString ( ( DBIDRef ) id ) ) . append ( " " ) . append ( column ) . append ( " " ) . append ( score ) ; }
Append to a text buffer .
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protected Cluster < SubspaceModel > runDOC ( Database database , Relation < V > relation , ArrayModifiableDBIDs S , final int d , int n , int m , int r , int minClusterSize ) { // Best cluster for the current run. DBIDs C = null ; // Relevant attributes for the best cluster. long [ ] D = null ; // Quality of the best cluster. double quality = Double . NEGATIVE_INFINITY ; // Bounds for our cluster. // ModifiableHyperBoundingBox bounds = new ModifiableHyperBoundingBox(new // double[d], new double[d]); // Inform the user about the progress in the current iteration. FiniteProgress iprogress = LOG . isVerbose ( ) ? new FiniteProgress ( "Iteration progress for current cluster" , m * n , LOG ) : null ; Random random = rnd . getSingleThreadedRandom ( ) ; DBIDArrayIter iter = S . iter ( ) ; for ( int i = 0 ; i < n ; ++ i ) { // Pick a random seed point. iter . seek ( random . nextInt ( S . size ( ) ) ) ; for ( int j = 0 ; j < m ; ++ j ) { // Choose a set of random points. DBIDs randomSet = DBIDUtil . randomSample ( S , r , random ) ; // Initialize cluster info. long [ ] nD = BitsUtil . zero ( d ) ; // Test each dimension and build bounding box. for ( int k = 0 ; k < d ; ++ k ) { if ( dimensionIsRelevant ( k , relation , randomSet ) ) { BitsUtil . setI ( nD , k ) ; } } if ( BitsUtil . cardinality ( nD ) > 0 ) { DBIDs nC = findNeighbors ( iter , nD , S , relation ) ; if ( LOG . isDebuggingFiner ( ) ) { LOG . finer ( "Testing a cluster candidate, |C| = " + nC . size ( ) + ", |D| = " + BitsUtil . cardinality ( nD ) ) ; } // Is the cluster large enough? if ( nC . size ( ) < minClusterSize ) { // Too small. if ( LOG . isDebuggingFiner ( ) ) { LOG . finer ( "... but it's too small." ) ; } continue ; } // Better cluster than before? double nQuality = computeClusterQuality ( nC . size ( ) , BitsUtil . cardinality ( nD ) ) ; if ( nQuality > quality ) { if ( LOG . isDebuggingFiner ( ) ) { LOG . finer ( "... and it's the best so far: " + nQuality + " vs. " + quality ) ; } C = nC ; D = nD ; quality = nQuality ; } else { if ( LOG . isDebuggingFiner ( ) ) { LOG . finer ( "... but we already have a better one." ) ; } } } LOG . incrementProcessed ( iprogress ) ; } } LOG . ensureCompleted ( iprogress ) ; return ( C != null ) ? makeCluster ( relation , C , D ) : null ; }
Performs a single run of DOC finding a single cluster .
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protected DBIDs findNeighbors ( DBIDRef q , long [ ] nD , ArrayModifiableDBIDs S , Relation < V > relation ) { // Weights for distance (= rectangle query) DistanceQuery < V > dq = relation . getDistanceQuery ( new SubspaceMaximumDistanceFunction ( nD ) ) ; // TODO: add filtering capabilities into query API! // Until then, using the range query API will be unnecessarily slow. // RangeQuery<V> rq = relation.getRangeQuery(df, DatabaseQuery.HINT_SINGLE); ArrayModifiableDBIDs nC = DBIDUtil . newArray ( ) ; for ( DBIDIter it = S . iter ( ) ; it . valid ( ) ; it . advance ( ) ) { if ( dq . distance ( q , it ) <= w ) { nC . add ( it ) ; } } return nC ; }
Find the neighbors of point q in the given subspace
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protected Cluster < SubspaceModel > makeCluster ( Relation < V > relation , DBIDs C , long [ ] D ) { DBIDs ids = DBIDUtil . newHashSet ( C ) ; // copy, also to lose distance values! Cluster < SubspaceModel > cluster = new Cluster <> ( ids ) ; cluster . setModel ( new SubspaceModel ( new Subspace ( D ) , Centroid . make ( relation , ids ) . getArrayRef ( ) ) ) ; return cluster ; }
Utility method to create a subspace cluster from a list of DBIDs and the relevant attributes .
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protected static < M extends Model > int [ ] findDepth ( Clustering < M > c ) { final Hierarchy < Cluster < M > > hier = c . getClusterHierarchy ( ) ; int [ ] size = { 0 , 0 } ; for ( It < Cluster < M > > iter = c . iterToplevelClusters ( ) ; iter . valid ( ) ; iter . advance ( ) ) { findDepth ( hier , iter . get ( ) , size ) ; } return size ; }
Compute the size of the clustering .
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private static < M extends Model > void findDepth ( Hierarchy < Cluster < M > > hier , Cluster < M > cluster , int [ ] size ) { if ( hier . numChildren ( cluster ) > 0 ) { for ( It < Cluster < M > > iter = hier . iterChildren ( cluster ) ; iter . valid ( ) ; iter . advance ( ) ) { findDepth ( hier , iter . get ( ) , size ) ; } size [ 0 ] += 1 ; // Depth } else { size [ 1 ] += 1 ; // Leaves } }
Recursive depth computation .
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protected static int getPreferredColumns ( double width , double height , int numc , double maxwidth ) { // Maximum width (compared to height) of labels - guess. // FIXME: do we really need to do this three-step computation? // Number of rows we'd use in a squared layout: final double rows = Math . ceil ( FastMath . pow ( numc * maxwidth , height / ( width + height ) ) ) ; // Given this number of rows (plus one for header), use this many columns: return ( int ) Math . ceil ( numc / ( rows + 1 ) ) ; }
Compute the preferred number of columns .
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public ELKIBuilder < T > with ( String opt , Object value ) { p . addParameter ( opt , value ) ; return this ; }
Add an option to the builder .
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@ SuppressWarnings ( "unchecked" ) public < C extends T > C build ( ) { if ( p == null ) { throw new AbortException ( "build() may be called only once." ) ; } final T obj = ClassGenericsUtil . parameterizeOrAbort ( clazz , p ) ; if ( p . hasUnusedParameters ( ) ) { LOG . warning ( "Unused parameters: " + p . getRemainingParameters ( ) ) ; } p = null ; // Prevent build() from being called again. return ( C ) obj ; }
Instantiate consuming the parameter list .
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protected static double [ ] [ ] randomInitialSolution ( final int size , final int dim , Random random ) { double [ ] [ ] sol = new double [ size ] [ dim ] ; for ( int i = 0 ; i < size ; i ++ ) { for ( int j = 0 ; j < dim ; j ++ ) { sol [ i ] [ j ] = random . nextGaussian ( ) * INITIAL_SOLUTION_SCALE ; } } return sol ; }
Generate a random initial solution .
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protected double sqDist ( double [ ] v1 , double [ ] v2 ) { assert ( v1 . length == v2 . length ) : "Lengths do not agree: " + v1 . length + " " + v2 . length ; double sum = 0 ; for ( int i = 0 ; i < v1 . length ; i ++ ) { final double diff = v1 [ i ] - v2 [ i ] ; sum += diff * diff ; } ++ projectedDistances ; return sum ; }
Squared distance in projection space .
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protected void updateSolution ( double [ ] [ ] sol , double [ ] meta , int it ) { final double mom = ( it < momentumSwitch && initialMomentum < finalMomentum ) ? initialMomentum : finalMomentum ; final int dim3 = dim * 3 ; for ( int i = 0 , off = 0 ; i < sol . length ; i ++ , off += dim3 ) { final double [ ] sol_i = sol [ i ] ; for ( int k = 0 ; k < dim ; k ++ ) { // Indexes in meta array final int gradk = off + k , movk = gradk + dim , gaink = movk + dim ; // Adjust learning rate: meta [ gaink ] = MathUtil . max ( ( ( meta [ gradk ] > 0 ) != ( meta [ movk ] > 0 ) ) ? ( meta [ gaink ] + 0.2 ) : ( meta [ gaink ] * 0.8 ) , MIN_GAIN ) ; meta [ movk ] *= mom ; // Dampening the previous momentum meta [ movk ] -= learningRate * meta [ gradk ] * meta [ gaink ] ; // Learn sol_i [ k ] += meta [ movk ] ; } } }
Update the current solution on iteration .
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private int getOffset ( int x , int y ) { return ( y < x ) ? ( triangleSize ( x ) + y ) : ( triangleSize ( y ) + x ) ; }
Array offset computation .
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@ Override public double getWeight ( double distance , double max , double stddev ) { if ( stddev <= 0 ) { return 1 ; } double scaleddistance = distance / ( scaling * stddev ) ; // After this, the result would be negative. if ( scaleddistance >= 1.0 ) { return 0.0 ; } return 1.0 - scaleddistance * scaleddistance ; }
Evaluate weight function at given parameters . max is ignored .
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public OPTICSPlot getOPTICSPlot ( VisualizerContext context ) { if ( plot == null ) { plot = OPTICSPlot . plotForClusterOrder ( clusterOrder , context ) ; } return plot ; }
Get or produce the actual OPTICS plot .
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public void setValue ( boolean val ) { try { super . setValue ( Boolean . valueOf ( val ) ) ; } catch ( ParameterException e ) { // We're pretty sure that any Boolean is okay, so this should never be // reached. throw new AbortException ( "Flag did not accept boolean value!" , e ) ; } }
Convenience function using a native boolean that doesn t require error handling .
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public OutlierResult run ( Database database , Relation < O > relation ) { StepProgress stepprog = LOG . isVerbose ( ) ? new StepProgress ( 5 ) : null ; Pair < KNNQuery < O > , KNNQuery < O > > pair = getKNNQueries ( database , relation , stepprog ) ; KNNQuery < O > knnComp = pair . getFirst ( ) ; KNNQuery < O > knnReach = pair . getSecond ( ) ; // Assert we got something if ( knnComp == null ) { throw new AbortException ( "No kNN queries supported by database for comparison distance function." ) ; } if ( knnReach == null ) { throw new AbortException ( "No kNN queries supported by database for density estimation distance function." ) ; } // FIXME: tie handling! // Probabilistic distances WritableDoubleDataStore pdists = DataStoreUtil . makeDoubleStorage ( relation . getDBIDs ( ) , DataStoreFactory . HINT_HOT | DataStoreFactory . HINT_DB ) ; LOG . beginStep ( stepprog , 3 , "Computing pdists" ) ; computePDists ( relation , knnReach , pdists ) ; // Compute PLOF values. WritableDoubleDataStore plofs = DataStoreUtil . makeDoubleStorage ( relation . getDBIDs ( ) , DataStoreFactory . HINT_HOT | DataStoreFactory . HINT_TEMP ) ; LOG . beginStep ( stepprog , 4 , "Computing PLOF" ) ; double nplof = computePLOFs ( relation , knnComp , pdists , plofs ) ; // Normalize the outlier scores. DoubleMinMax mm = new DoubleMinMax ( ) ; { // compute LOOP_SCORE of each db object LOG . beginStep ( stepprog , 5 , "Computing LoOP scores" ) ; FiniteProgress progressLOOPs = LOG . isVerbose ( ) ? new FiniteProgress ( "LoOP for objects" , relation . size ( ) , LOG ) : null ; final double norm = 1. / ( nplof * MathUtil . SQRT2 ) ; for ( DBIDIter iditer = relation . iterDBIDs ( ) ; iditer . valid ( ) ; iditer . advance ( ) ) { double loop = NormalDistribution . erf ( ( plofs . doubleValue ( iditer ) - 1. ) * norm ) ; plofs . putDouble ( iditer , loop ) ; mm . put ( loop ) ; LOG . incrementProcessed ( progressLOOPs ) ; } LOG . ensureCompleted ( progressLOOPs ) ; } LOG . setCompleted ( stepprog ) ; // Build result representation. DoubleRelation scoreResult = new MaterializedDoubleRelation ( "Local Outlier Probabilities" , "loop-outlier" , plofs , relation . getDBIDs ( ) ) ; OutlierScoreMeta scoreMeta = new ProbabilisticOutlierScore ( mm . getMin ( ) , mm . getMax ( ) , 0. ) ; return new OutlierResult ( scoreMeta , scoreResult ) ; }
Performs the LoOP algorithm on the given database .
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protected void computePDists ( Relation < O > relation , KNNQuery < O > knn , WritableDoubleDataStore pdists ) { // computing PRDs FiniteProgress prdsProgress = LOG . isVerbose ( ) ? new FiniteProgress ( "pdists" , relation . size ( ) , LOG ) : null ; for ( DBIDIter iditer = relation . iterDBIDs ( ) ; iditer . valid ( ) ; iditer . advance ( ) ) { final KNNList neighbors = knn . getKNNForDBID ( iditer , kreach + 1 ) ; // + // query // point // use first kref neighbors as reference set int ks = 0 ; double ssum = 0. ; for ( DoubleDBIDListIter neighbor = neighbors . iter ( ) ; neighbor . valid ( ) && ks < kreach ; neighbor . advance ( ) ) { if ( DBIDUtil . equal ( neighbor , iditer ) ) { continue ; } final double d = neighbor . doubleValue ( ) ; ssum += d * d ; ks ++ ; } double pdist = ks > 0 ? FastMath . sqrt ( ssum / ks ) : 0. ; pdists . putDouble ( iditer , pdist ) ; LOG . incrementProcessed ( prdsProgress ) ; } LOG . ensureCompleted ( prdsProgress ) ; }
Compute the probabilistic distances used by LoOP .
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protected double computePLOFs ( Relation < O > relation , KNNQuery < O > knn , WritableDoubleDataStore pdists , WritableDoubleDataStore plofs ) { FiniteProgress progressPLOFs = LOG . isVerbose ( ) ? new FiniteProgress ( "PLOFs for objects" , relation . size ( ) , LOG ) : null ; double nplof = 0. ; for ( DBIDIter iditer = relation . iterDBIDs ( ) ; iditer . valid ( ) ; iditer . advance ( ) ) { final KNNList neighbors = knn . getKNNForDBID ( iditer , kcomp + 1 ) ; // + query // point // use first kref neighbors as comparison set. int ks = 0 ; double sum = 0. ; for ( DBIDIter neighbor = neighbors . iter ( ) ; neighbor . valid ( ) && ks < kcomp ; neighbor . advance ( ) ) { if ( DBIDUtil . equal ( neighbor , iditer ) ) { continue ; } sum += pdists . doubleValue ( neighbor ) ; ks ++ ; } double plof = MathUtil . max ( pdists . doubleValue ( iditer ) * ks / sum , 1.0 ) ; if ( Double . isNaN ( plof ) || Double . isInfinite ( plof ) ) { plof = 1.0 ; } plofs . putDouble ( iditer , plof ) ; nplof += ( plof - 1.0 ) * ( plof - 1.0 ) ; LOG . incrementProcessed ( progressPLOFs ) ; } LOG . ensureCompleted ( progressPLOFs ) ; nplof = lambda * FastMath . sqrt ( nplof / relation . size ( ) ) ; if ( LOG . isDebuggingFine ( ) ) { LOG . debugFine ( "nplof normalization factor is " + nplof ) ; } return nplof > 0. ? nplof : 1. ; }
Compute the LOF values using the pdist distances .
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@ SuppressWarnings ( "unchecked" ) public final void writeObject ( TextWriterStream out , String label , Object object ) throws IOException { write ( out , label , ( O ) object ) ; }
Non - type - checking version .
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@ Override public int filter ( double [ ] eigenValues ) { // determine sum of eigenvalues double totalSum = 0 ; for ( int i = 0 ; i < eigenValues . length ; i ++ ) { totalSum += eigenValues [ i ] ; } double expectedVariance = totalSum / eigenValues . length * walpha ; // determine strong and weak eigenpairs double currSum = 0 ; for ( int i = 0 ; i < eigenValues . length - 1 ; i ++ ) { // weak Eigenvector? if ( eigenValues [ i ] < expectedVariance ) { break ; } currSum += eigenValues [ i ] ; // calculate progressive alpha level double alpha = 1.0 - ( 1.0 - palpha ) * ( 1.0 - ( i + 1 ) / ( double ) eigenValues . length ) ; if ( currSum / totalSum >= alpha ) { return i + 1 ; } } // the code using this method doesn't expect an empty strong set, // if we didn't find any strong ones, we make all vectors strong return eigenValues . length ; }
Filter eigenpairs .
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public static List < OutlierResult > getOutlierResults ( Result r ) { if ( r instanceof OutlierResult ) { List < OutlierResult > ors = new ArrayList <> ( 1 ) ; ors . add ( ( OutlierResult ) r ) ; return ors ; } if ( r instanceof HierarchicalResult ) { return ResultUtil . filterResults ( ( ( HierarchicalResult ) r ) . getHierarchy ( ) , r , OutlierResult . class ) ; } return Collections . emptyList ( ) ; }
Collect all outlier results from a Result
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double evaluateBy ( ScoreEvaluation eval ) { return eval . evaluate ( new DBIDsTest ( DBIDUtil . ensureSet ( scores . getDBIDs ( ) ) ) , new OutlierScoreAdapter ( this ) ) ; }
Evaluate given a set of positives and a scoring .
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public double distance ( double [ ] v1 , double [ ] v2 ) { final int dim1 = v1 . length , dim2 = v2 . length ; final int mindim = dim1 < dim2 ? dim1 : dim2 ; double agg = preDistance ( v1 , v2 , 0 , mindim ) ; if ( dim1 > mindim ) { agg += preNorm ( v1 , mindim , dim1 ) ; } else if ( dim2 > mindim ) { agg += preNorm ( v2 , mindim , dim2 ) ; } return agg ; }
Special version for double arrays .
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private void computeWeights ( Node node ) { int wsum = 0 ; for ( Node child : node . children ) { computeWeights ( child ) ; wsum += child . weight ; } node . weight = Math . max ( 1 , wsum ) ; }
Recursively assign node weights .
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@ Override public void processNewResult ( ResultHierarchy hier , Result result ) { // Get all new clusterings // TODO: handle clusterings added later, too. Can we update the result? List < Clustering < ? > > clusterings = Clustering . getClusteringResults ( result ) ; // Abort if not enough clusterings to compare if ( clusterings . size ( ) < 2 ) { return ; } // create segments Segments segments = new Segments ( clusterings ) ; hier . add ( result , segments ) ; }
Perform clusterings evaluation
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public double getPartialMinDist ( int dimension , int vp ) { final int qp = queryApprox . getApproximation ( dimension ) ; if ( vp < qp ) { return lookup [ dimension ] [ vp + 1 ] ; } else if ( vp > qp ) { return lookup [ dimension ] [ vp ] ; } else { return 0.0 ; } }
Get the minimum distance contribution of a single dimension .
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public double getMinDist ( VectorApproximation vec ) { final int dim = lookup . length ; double minDist = 0 ; for ( int d = 0 ; d < dim ; d ++ ) { final int vp = vec . getApproximation ( d ) ; minDist += getPartialMinDist ( d , vp ) ; } return FastMath . pow ( minDist , onebyp ) ; }
Get the minimum distance to approximated vector vec .
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public double getPartialMaxDist ( int dimension , int vp ) { final int qp = queryApprox . getApproximation ( dimension ) ; if ( vp < qp ) { return lookup [ dimension ] [ vp ] ; } else if ( vp > qp ) { return lookup [ dimension ] [ vp + 1 ] ; } else { return Math . max ( lookup [ dimension ] [ vp ] , lookup [ dimension ] [ vp + 1 ] ) ; } }
Get the maximum distance contribution of a single dimension .
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private void initializeLookupTable ( double [ ] [ ] splitPositions , NumberVector query , double p ) { final int dimensions = splitPositions . length ; final int bordercount = splitPositions [ 0 ] . length ; lookup = new double [ dimensions ] [ bordercount ] ; for ( int d = 0 ; d < dimensions ; d ++ ) { final double val = query . doubleValue ( d ) ; for ( int i = 0 ; i < bordercount ; i ++ ) { lookup [ d ] [ i ] = FastMath . pow ( splitPositions [ d ] [ i ] - val , p ) ; } } }
Initialize the lookup table .
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protected void makeStyleResult ( StyleLibrary stylelib ) { final Database db = ResultUtil . findDatabase ( hier ) ; stylelibrary = stylelib ; List < Clustering < ? extends Model > > clusterings = Clustering . getClusteringResults ( db ) ; if ( ! clusterings . isEmpty ( ) ) { stylepolicy = new ClusterStylingPolicy ( clusterings . get ( 0 ) , stylelib ) ; } else { Clustering < Model > c = generateDefaultClustering ( ) ; stylepolicy = new ClusterStylingPolicy ( c , stylelib ) ; } }
Generate a new style result for the given style library .
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@ Override public void contentChanged ( DataStoreEvent e ) { for ( int i = 0 ; i < listenerList . size ( ) ; i ++ ) { listenerList . get ( i ) . contentChanged ( e ) ; } }
Proxy datastore event to child listeners .
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private void notifyFactories ( Object item ) { for ( VisualizationProcessor f : factories ) { try { f . processNewResult ( this , item ) ; } catch ( Throwable e ) { LOG . warning ( "VisFactory " + f . getClass ( ) . getCanonicalName ( ) + " failed:" , e ) ; } } }
Notify factories of a change .
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protected int chooseBulkSplitPoint ( int numEntries , int minEntries , int maxEntries ) { if ( numEntries < minEntries ) { throw new IllegalArgumentException ( "numEntries < minEntries!" ) ; } if ( numEntries <= maxEntries ) { return numEntries ; } else if ( numEntries < maxEntries + minEntries ) { return ( numEntries - minEntries ) ; } else { return maxEntries ; } }
Computes and returns the best split point .
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protected < T > List < List < T > > trivialPartition ( List < T > objects , int minEntries , int maxEntries ) { // build partitions final int size = objects . size ( ) ; final int numberPartitions = ( int ) Math . ceil ( ( ( double ) size ) / maxEntries ) ; List < List < T > > partitions = new ArrayList <> ( numberPartitions ) ; int start = 0 ; for ( int pnum = 0 ; pnum < numberPartitions ; pnum ++ ) { int end = ( int ) ( ( pnum + 1. ) * size / numberPartitions ) ; if ( pnum == numberPartitions - 1 ) { end = size ; } assert ( ( end - start ) >= minEntries && ( end - start ) <= maxEntries ) ; partitions . add ( objects . subList ( start , end ) ) ; start = end ; } return partitions ; }
Perform the trivial partitioning of the given list .
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protected Relation < ? > [ ] alignColumns ( ObjectBundle pack ) { // align representations. Relation < ? > [ ] targets = new Relation < ? > [ pack . metaLength ( ) ] ; long [ ] used = BitsUtil . zero ( relations . size ( ) ) ; for ( int i = 0 ; i < targets . length ; i ++ ) { SimpleTypeInformation < ? > meta = pack . meta ( i ) ; // TODO: aggressively try to match exact metas first? // Try to match unused representations only for ( int j = BitsUtil . nextClearBit ( used , 0 ) ; j >= 0 && j < relations . size ( ) ; j = BitsUtil . nextClearBit ( used , j + 1 ) ) { Relation < ? > relation = relations . get ( j ) ; if ( relation . getDataTypeInformation ( ) . isAssignableFromType ( meta ) ) { targets [ i ] = relation ; BitsUtil . setI ( used , j ) ; break ; } } if ( targets [ i ] == null ) { targets [ i ] = addNewRelation ( meta ) ; BitsUtil . setI ( used , relations . size ( ) - 1 ) ; } } return targets ; }
Find a mapping from package columns to database columns eventually adding new database columns when needed .
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private Relation < ? > addNewRelation ( SimpleTypeInformation < ? > meta ) { @ SuppressWarnings ( "unchecked" ) SimpleTypeInformation < Object > ometa = ( SimpleTypeInformation < Object > ) meta ; Relation < ? > relation = new MaterializedRelation <> ( ometa , ids ) ; relations . add ( relation ) ; getHierarchy ( ) . add ( this , relation ) ; // Try to add indexes where appropriate for ( IndexFactory < ? > factory : indexFactories ) { if ( factory . getInputTypeRestriction ( ) . isAssignableFromType ( meta ) ) { @ SuppressWarnings ( "unchecked" ) final IndexFactory < Object > ofact = ( IndexFactory < Object > ) factory ; @ SuppressWarnings ( "unchecked" ) final Relation < Object > orep = ( Relation < Object > ) relation ; Index index = ofact . instantiate ( orep ) ; index . initialize ( ) ; getHierarchy ( ) . add ( relation , index ) ; } } return relation ; }
Add a new representation for the given meta .
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private void doDelete ( DBIDRef id ) { // Remove id ids . remove ( id ) ; // Remove from all representations. for ( Relation < ? > relation : relations ) { // ID has already been removed, and this would loop... if ( relation == idrep ) { continue ; } if ( ! ( relation instanceof ModifiableRelation ) ) { throw new AbortException ( "Non-modifiable relations have been added to the database." ) ; } ( ( ModifiableRelation < ? > ) relation ) . delete ( id ) ; } DBIDFactory . FACTORY . deallocateSingleDBID ( id ) ; }
Removes the object with the specified id from this database .
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private ArrayDBIDs greedy ( DistanceQuery < V > distFunc , DBIDs sampleSet , int m , Random random ) { ArrayModifiableDBIDs medoids = DBIDUtil . newArray ( m ) ; ArrayModifiableDBIDs s = DBIDUtil . newArray ( sampleSet ) ; DBIDArrayIter iter = s . iter ( ) ; DBIDVar m_i = DBIDUtil . newVar ( ) ; int size = s . size ( ) ; // Move a random element to the end, then pop() s . swap ( random . nextInt ( size ) , -- size ) ; medoids . add ( s . pop ( m_i ) ) ; if ( LOG . isDebugging ( ) ) { LOG . debugFiner ( "medoids " + medoids . toString ( ) ) ; } // To track the current worst element: int worst = - 1 ; double worstd = Double . NEGATIVE_INFINITY ; // compute distances between each point in S and m_i WritableDoubleDataStore distances = DataStoreUtil . makeDoubleStorage ( s , DataStoreFactory . HINT_HOT | DataStoreFactory . HINT_TEMP ) ; for ( iter . seek ( 0 ) ; iter . getOffset ( ) < size ; iter . advance ( ) ) { final double dist = distFunc . distance ( iter , m_i ) ; distances . putDouble ( iter , dist ) ; if ( dist > worstd ) { worstd = dist ; worst = iter . getOffset ( ) ; } } for ( int i = 1 ; i < m ; i ++ ) { // choose medoid m_i to be far from previous medoids s . swap ( worst , -- size ) ; medoids . add ( s . pop ( m_i ) ) ; // compute distances of each point to closest medoid; track worst. worst = - 1 ; worstd = Double . NEGATIVE_INFINITY ; for ( iter . seek ( 0 ) ; iter . getOffset ( ) < size ; iter . advance ( ) ) { double dist_new = distFunc . distance ( iter , m_i ) ; double dist_old = distances . doubleValue ( iter ) ; double dist = ( dist_new < dist_old ) ? dist_new : dist_old ; distances . putDouble ( iter , dist ) ; if ( dist > worstd ) { worstd = dist ; worst = iter . getOffset ( ) ; } } if ( LOG . isDebugging ( ) ) { LOG . debugFiner ( "medoids " + medoids . toString ( ) ) ; } } return medoids ; }
Returns a piercing set of k medoids from the specified sample set .
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private ArrayDBIDs initialSet ( DBIDs sampleSet , int k , Random random ) { return DBIDUtil . ensureArray ( DBIDUtil . randomSample ( sampleSet , k , random ) ) ; }
Returns a set of k elements from the specified sample set .
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private ArrayDBIDs computeM_current ( DBIDs m , DBIDs m_best , DBIDs m_bad , Random random ) { ArrayModifiableDBIDs m_list = DBIDUtil . newArray ( m ) ; m_list . removeDBIDs ( m_best ) ; DBIDArrayMIter it = m_list . iter ( ) ; ArrayModifiableDBIDs m_current = DBIDUtil . newArray ( ) ; for ( DBIDIter iter = m_best . iter ( ) ; iter . valid ( ) ; iter . advance ( ) ) { if ( m_bad . contains ( iter ) ) { int currentSize = m_current . size ( ) ; while ( m_current . size ( ) == currentSize ) { m_current . add ( it . seek ( random . nextInt ( m_list . size ( ) ) ) ) ; it . remove ( ) ; } } else { m_current . add ( iter ) ; } } return m_current ; }
Computes the set of medoids in current iteration .
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private long [ ] [ ] findDimensions ( ArrayDBIDs medoids , Relation < V > database , DistanceQuery < V > distFunc , RangeQuery < V > rangeQuery ) { // get localities DataStore < DBIDs > localities = getLocalities ( medoids , distFunc , rangeQuery ) ; // compute x_ij = avg distance from points in l_i to medoid m_i final int dim = RelationUtil . dimensionality ( database ) ; final int numc = medoids . size ( ) ; double [ ] [ ] averageDistances = new double [ numc ] [ ] ; for ( DBIDArrayIter iter = medoids . iter ( ) ; iter . valid ( ) ; iter . advance ( ) ) { V medoid_i = database . get ( iter ) ; DBIDs l_i = localities . get ( iter ) ; double [ ] x_i = new double [ dim ] ; for ( DBIDIter qr = l_i . iter ( ) ; qr . valid ( ) ; qr . advance ( ) ) { V o = database . get ( qr ) ; for ( int d = 0 ; d < dim ; d ++ ) { x_i [ d ] += Math . abs ( medoid_i . doubleValue ( d ) - o . doubleValue ( d ) ) ; } } for ( int d = 0 ; d < dim ; d ++ ) { x_i [ d ] /= l_i . size ( ) ; } averageDistances [ iter . getOffset ( ) ] = x_i ; } List < DoubleIntInt > z_ijs = computeZijs ( averageDistances , dim ) ; return computeDimensionMap ( z_ijs , dim , numc ) ; }
Determines the set of correlated dimensions for each medoid in the specified medoid set .
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private List < Pair < double [ ] , long [ ] > > findDimensions ( ArrayList < PROCLUSCluster > clusters , Relation < V > database ) { // compute x_ij = avg distance from points in c_i to c_i.centroid final int dim = RelationUtil . dimensionality ( database ) ; final int numc = clusters . size ( ) ; double [ ] [ ] averageDistances = new double [ numc ] [ ] ; for ( int i = 0 ; i < numc ; i ++ ) { PROCLUSCluster c_i = clusters . get ( i ) ; double [ ] x_i = new double [ dim ] ; for ( DBIDIter iter = c_i . objectIDs . iter ( ) ; iter . valid ( ) ; iter . advance ( ) ) { V o = database . get ( iter ) ; for ( int d = 0 ; d < dim ; d ++ ) { x_i [ d ] += Math . abs ( c_i . centroid [ d ] - o . doubleValue ( d ) ) ; } } for ( int d = 0 ; d < dim ; d ++ ) { x_i [ d ] /= c_i . objectIDs . size ( ) ; } averageDistances [ i ] = x_i ; } List < DoubleIntInt > z_ijs = computeZijs ( averageDistances , dim ) ; long [ ] [ ] dimensionMap = computeDimensionMap ( z_ijs , dim , numc ) ; // mapping cluster -> dimensions List < Pair < double [ ] , long [ ] > > result = new ArrayList <> ( numc ) ; for ( int i = 0 ; i < numc ; i ++ ) { long [ ] dims_i = dimensionMap [ i ] ; if ( dims_i == null ) { continue ; } result . add ( new Pair <> ( clusters . get ( i ) . centroid , dims_i ) ) ; } return result ; }
Refinement step that determines the set of correlated dimensions for each cluster centroid .
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private List < DoubleIntInt > computeZijs ( double [ ] [ ] averageDistances , final int dim ) { List < DoubleIntInt > z_ijs = new ArrayList <> ( averageDistances . length * dim ) ; for ( int i = 0 ; i < averageDistances . length ; i ++ ) { double [ ] x_i = averageDistances [ i ] ; // y_i double y_i = 0 ; for ( int j = 0 ; j < dim ; j ++ ) { y_i += x_i [ j ] ; } y_i /= dim ; // sigma_i double sigma_i = 0 ; for ( int j = 0 ; j < dim ; j ++ ) { double diff = x_i [ j ] - y_i ; sigma_i += diff * diff ; } sigma_i /= ( dim - 1 ) ; sigma_i = FastMath . sqrt ( sigma_i ) ; for ( int j = 0 ; j < dim ; j ++ ) { z_ijs . add ( new DoubleIntInt ( ( x_i [ j ] - y_i ) / sigma_i , i , j ) ) ; } } Collections . sort ( z_ijs ) ; return z_ijs ; }
Compute the z_ij values .
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private long [ ] [ ] computeDimensionMap ( List < DoubleIntInt > z_ijs , final int dim , final int numc ) { // mapping cluster index -> dimensions long [ ] [ ] dimensionMap = new long [ numc ] [ ( ( dim - 1 ) >> 6 ) + 1 ] ; int max = Math . max ( k * l , 2 ) ; for ( int m = 0 ; m < max ; m ++ ) { DoubleIntInt z_ij = z_ijs . get ( m ) ; long [ ] dims_i = dimensionMap [ z_ij . dimi ] ; BitsUtil . setI ( dims_i , z_ij . dimj ) ; if ( LOG . isDebugging ( ) ) { LOG . debugFiner ( new StringBuilder ( ) . append ( "z_ij " ) . append ( z_ij ) . append ( ' ' ) // . append ( "D_i " ) . append ( BitsUtil . toString ( dims_i ) ) . toString ( ) ) ; } } return dimensionMap ; }
Compute the dimension map .
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private ArrayList < PROCLUSCluster > assignPoints ( ArrayDBIDs m_current , long [ ] [ ] dimensions , Relation < V > database ) { ModifiableDBIDs [ ] clusterIDs = new ModifiableDBIDs [ dimensions . length ] ; for ( int i = 0 ; i < m_current . size ( ) ; i ++ ) { clusterIDs [ i ] = DBIDUtil . newHashSet ( ) ; } DBIDArrayIter m_i = m_current . iter ( ) ; for ( DBIDIter it = database . iterDBIDs ( ) ; it . valid ( ) ; it . advance ( ) ) { V p = database . get ( it ) ; double minDist = Double . NaN ; int best = - 1 , i = 0 ; for ( m_i . seek ( 0 ) ; m_i . valid ( ) ; m_i . advance ( ) , i ++ ) { V m = database . get ( m_i ) ; double currentDist = manhattanSegmentalDistance ( p , m , dimensions [ i ] ) ; if ( ! ( minDist <= currentDist ) ) { minDist = currentDist ; best = i ; } } // add p to cluster with mindist assert best >= 0 ; clusterIDs [ best ] . add ( it ) ; } ArrayList < PROCLUSCluster > clusters = new ArrayList <> ( m_current . size ( ) ) ; for ( int i = 0 ; i < dimensions . length ; i ++ ) { ModifiableDBIDs objectIDs = clusterIDs [ i ] ; if ( ! objectIDs . isEmpty ( ) ) { long [ ] clusterDimensions = dimensions [ i ] ; double [ ] centroid = Centroid . make ( database , objectIDs ) . getArrayRef ( ) ; clusters . add ( new PROCLUSCluster ( objectIDs , clusterDimensions , centroid ) ) ; } else { clusters . add ( null ) ; } } if ( LOG . isDebugging ( ) ) { LOG . debugFine ( new StringBuilder ( ) . append ( "clusters " ) . append ( clusters ) . toString ( ) ) ; } return clusters ; }
Assigns the objects to the clusters .
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private List < PROCLUSCluster > finalAssignment ( List < Pair < double [ ] , long [ ] > > dimensions , Relation < V > database ) { Map < Integer , ModifiableDBIDs > clusterIDs = new HashMap <> ( ) ; for ( int i = 0 ; i < dimensions . size ( ) ; i ++ ) { clusterIDs . put ( i , DBIDUtil . newHashSet ( ) ) ; } for ( DBIDIter it = database . iterDBIDs ( ) ; it . valid ( ) ; it . advance ( ) ) { V p = database . get ( it ) ; double minDist = Double . POSITIVE_INFINITY ; int best = - 1 ; for ( int i = 0 ; i < dimensions . size ( ) ; i ++ ) { Pair < double [ ] , long [ ] > pair_i = dimensions . get ( i ) ; double currentDist = manhattanSegmentalDistance ( p , pair_i . first , pair_i . second ) ; if ( best < 0 || currentDist < minDist ) { minDist = currentDist ; best = i ; } } // add p to cluster with mindist assert minDist >= 0. ; clusterIDs . get ( best ) . add ( it ) ; } List < PROCLUSCluster > clusters = new ArrayList <> ( ) ; for ( int i = 0 ; i < dimensions . size ( ) ; i ++ ) { ModifiableDBIDs objectIDs = clusterIDs . get ( i ) ; if ( ! objectIDs . isEmpty ( ) ) { long [ ] clusterDimensions = dimensions . get ( i ) . second ; double [ ] centroid = Centroid . make ( database , objectIDs ) . getArrayRef ( ) ; clusters . add ( new PROCLUSCluster ( objectIDs , clusterDimensions , centroid ) ) ; } } if ( LOG . isDebugging ( ) ) { LOG . debugFine ( new StringBuilder ( ) . append ( "clusters " ) . append ( clusters ) . toString ( ) ) ; } return clusters ; }
Refinement step to assign the objects to the final clusters .
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private double manhattanSegmentalDistance ( NumberVector o1 , double [ ] o2 , long [ ] dimensions ) { double result = 0 ; int card = 0 ; for ( int d = BitsUtil . nextSetBit ( dimensions , 0 ) ; d >= 0 ; d = BitsUtil . nextSetBit ( dimensions , d + 1 ) ) { result += Math . abs ( o1 . doubleValue ( d ) - o2 [ d ] ) ; ++ card ; } return result / card ; }
Returns the Manhattan segmental distance between o1 and o2 relative to the specified dimensions .
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private double evaluateClusters ( ArrayList < PROCLUSCluster > clusters , long [ ] [ ] dimensions , Relation < V > database ) { double result = 0 ; for ( int i = 0 ; i < dimensions . length ; i ++ ) { PROCLUSCluster c_i = clusters . get ( i ) ; double [ ] centroid_i = c_i . centroid ; long [ ] dims_i = dimensions [ i ] ; double w_i = 0 ; for ( int d = BitsUtil . nextSetBit ( dims_i , 0 ) ; d >= 0 ; d = BitsUtil . nextSetBit ( dims_i , d + 1 ) ) { w_i += avgDistance ( centroid_i , c_i . objectIDs , database , d ) ; } w_i /= dimensions . length ; result += c_i . objectIDs . size ( ) * w_i ; } return result / database . size ( ) ; }
Evaluates the quality of the clusters .
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private double avgDistance ( double [ ] centroid , DBIDs objectIDs , Relation < V > database , int dimension ) { Mean avg = new Mean ( ) ; for ( DBIDIter iter = objectIDs . iter ( ) ; iter . valid ( ) ; iter . advance ( ) ) { V o = database . get ( iter ) ; avg . put ( Math . abs ( centroid [ dimension ] - o . doubleValue ( dimension ) ) ) ; } return avg . getMean ( ) ; }
Computes the average distance of the objects to the centroid along the specified dimension .
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private DBIDs computeBadMedoids ( ArrayDBIDs m_current , ArrayList < PROCLUSCluster > clusters , int threshold ) { ModifiableDBIDs badMedoids = DBIDUtil . newHashSet ( m_current . size ( ) ) ; int i = 0 ; for ( DBIDIter it = m_current . iter ( ) ; it . valid ( ) ; it . advance ( ) , i ++ ) { PROCLUSCluster c_i = clusters . get ( i ) ; if ( c_i == null || c_i . objectIDs . size ( ) < threshold ) { badMedoids . add ( it ) ; } } return badMedoids ; }
Computes the bad medoids where the medoid of a cluster with less than the specified threshold of objects is bad .
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public static Bit valueOf ( String bit ) throws NumberFormatException { final int i = ParseUtil . parseIntBase10 ( bit ) ; if ( i != 0 && i != 1 ) { throw new NumberFormatException ( "Input \"" + bit + "\" must be 0 or 1." ) ; } return ( i > 0 ) ? TRUE : FALSE ; }
Method to construct a Bit for a given String expression .
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public ChangePoints run ( Relation < DoubleVector > relation ) { if ( ! ( relation . getDBIDs ( ) instanceof ArrayDBIDs ) ) { throw new AbortException ( "This implementation may only be used on static databases, with ArrayDBIDs to provide a clear order." ) ; } return new Instance ( rnd . getSingleThreadedRandom ( ) ) . run ( relation ) ; }
Executes multiple change point detection for given relation
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public static void cusum ( double [ ] data , double [ ] out , int begin , int end ) { assert ( out . length >= data . length ) ; // Use Kahan summation for better precision! // FIXME: this should be unit tested. double m = 0. , carry = 0. ; for ( int i = begin ; i < end ; i ++ ) { double v = data [ i ] - carry ; // Compensation double n = out [ i ] = ( m + v ) ; // May lose small digits of v. carry = ( n - m ) - v ; // Recover lost bits m = n ; } }
Compute the incremental sum of an array i . e . the sum of all points up to the given index .
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public static DoubleIntPair bestChangeInMean ( double [ ] sums , int begin , int end ) { final int len = end - begin , last = end - 1 ; final double suml = begin > 0 ? sums [ begin - 1 ] : 0. ; final double sumr = sums [ last ] ; int bestpos = begin ; double bestscore = Double . NEGATIVE_INFINITY ; // Iterate elements k=2..n-1 in math notation_ for ( int j = begin , km1 = 1 ; j < last ; j ++ , km1 ++ ) { assert ( km1 < len ) ; // FIXME: remove eventually final double sumj = sums [ j ] ; // Sum _inclusive_ j'th element. // Derive the left mean and right mean from the precomputed aggregates: final double lmean = ( sumj - suml ) / km1 ; final double rmean = ( sumr - sumj ) / ( len - km1 ) ; // Equation 2.6.17 from the Basseville book final double dm = lmean - rmean ; final double score = km1 * ( double ) ( len - km1 ) * dm * dm ; if ( score > bestscore ) { bestpos = j + 1 ; bestscore = score ; } } return new DoubleIntPair ( bestscore , bestpos ) ; }
Find the best position to assume a change in mean .
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public static void shuffle ( double [ ] bstrap , int len , Random rnd ) { int i = len ; while ( i > 0 ) { final int r = rnd . nextInt ( i ) ; -- i ; // Swap double tmp = bstrap [ r ] ; bstrap [ r ] = bstrap [ i ] ; bstrap [ i ] = tmp ; } }
Fisher - Yates shuffle of a partial array
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@ SuppressWarnings ( "unchecked" ) public T get ( double coord ) { if ( coord == Double . NEGATIVE_INFINITY ) { return getSpecial ( 0 ) ; } if ( coord == Double . POSITIVE_INFINITY ) { return getSpecial ( 1 ) ; } if ( Double . isNaN ( coord ) ) { return getSpecial ( 2 ) ; } int bin = getBinNr ( coord ) ; if ( bin < 0 ) { if ( size - bin > data . length ) { // Reallocate. TODO: use an arraylist-like grow strategy! Object [ ] tmpdata = new Object [ growSize ( data . length , size - bin ) ] ; System . arraycopy ( data , 0 , tmpdata , - bin , size ) ; data = tmpdata ; } else { // Shift in place System . arraycopy ( data , 0 , data , - bin , size ) ; } for ( int i = 0 ; i < - bin ; i ++ ) { data [ i ] = supplier . make ( ) ; } // Note that bin is negative, -bin is the shift offset! offset -= bin ; size -= bin ; // TODO: modCounter++; and have iterators fast-fail // Unset max value when resizing max = Double . MAX_VALUE ; return ( T ) data [ 0 ] ; } else if ( bin >= size ) { if ( bin >= data . length ) { Object [ ] tmpdata = new Object [ growSize ( data . length , bin + 1 ) ] ; System . arraycopy ( data , 0 , tmpdata , 0 , size ) ; data = tmpdata ; } for ( int i = size ; i <= bin ; i ++ ) { data [ i ] = supplier . make ( ) ; } size = bin + 1 ; // TODO: modCounter++; and have iterators fast-fail // Unset max value when resizing max = Double . MAX_VALUE ; return ( T ) data [ bin ] ; } else { return ( T ) data [ bin ] ; } }
Access the value of a bin with new data .
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protected void loadCache ( int size , InputStream in ) throws IOException { // Expect a sparse matrix here cache = new Long2FloatOpenHashMap ( size * 20 ) ; cache . defaultReturnValue ( Float . POSITIVE_INFINITY ) ; min = Integer . MAX_VALUE ; max = Integer . MIN_VALUE ; parser . parse ( in , new DistanceCacheWriter ( ) { @ Override public void put ( int id1 , int id2 , double distance ) { if ( id1 < id2 ) { min = id1 < min ? id1 : min ; max = id2 > max ? id2 : max ; } else { min = id2 < min ? id2 : min ; max = id1 > max ? id1 : max ; } cache . put ( makeKey ( id1 , id2 ) , ( float ) distance ) ; } } ) ; if ( min != 0 ) { LOG . verbose ( "Distance matrix is supposed to be 0-indexed. Choosing offset " + min + " to compensate." ) ; } if ( max + 1 - min != size ) { LOG . warning ( "ID range is not consistent with relation size." ) ; } }
Fill cache from an input stream .
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public Element svgElement ( String name , String cssclass ) { Element elem = SVGUtil . svgElement ( document , name ) ; if ( cssclass != null ) { elem . setAttribute ( SVGConstants . SVG_CLASS_ATTRIBUTE , cssclass ) ; } return elem ; }
Create a SVG element in the SVG namespace . Non - static version .
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public Element svgRect ( double x , double y , double w , double h ) { return SVGUtil . svgRect ( document , x , y , w , h ) ; }
Create a SVG rectangle
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public Element svgCircle ( double cx , double cy , double r ) { return SVGUtil . svgCircle ( document , cx , cy , r ) ; }
Create a SVG circle
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public Element svgLine ( double x1 , double y1 , double x2 , double y2 ) { return SVGUtil . svgLine ( document , x1 , y1 , x2 , y2 ) ; }
Create a SVG line element
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public SVGPoint elementCoordinatesFromEvent ( Element tag , Event evt ) { return SVGUtil . elementCoordinatesFromEvent ( document , tag , evt ) ; }
Convert screen coordinates to element coordinates .
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public void addCSSClassOrLogError ( CSSClass cls ) { try { cssman . addClass ( cls ) ; } catch ( CSSNamingConflict e ) { LoggingUtil . exception ( e ) ; } }
Convenience method to add a CSS class or log an error .
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public void updateStyleElement ( ) { // TODO: this should be sufficient - why does Batik occasionally not pick up // the changes unless we actually replace the style element itself? // cssman.updateStyleElement(document, style); Element newstyle = cssman . makeStyleElement ( document ) ; style . getParentNode ( ) . replaceChild ( newstyle , style ) ; style = newstyle ; }
Update style element - invoke this appropriately after any change to the CSS styles .
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public void saveAsSVG ( File file ) throws IOException , TransformerFactoryConfigurationError , TransformerException { OutputStream out = new BufferedOutputStream ( new FileOutputStream ( file ) ) ; // TODO embed linked images. javax . xml . transform . Result result = new StreamResult ( out ) ; SVGDocument doc = cloneDocument ( ) ; // Use a transformer for pretty printing Transformer xformer = TransformerFactory . newInstance ( ) . newTransformer ( ) ; xformer . setOutputProperty ( OutputKeys . INDENT , "yes" ) ; xformer . transform ( new DOMSource ( doc ) , result ) ; out . flush ( ) ; out . close ( ) ; }
Save document into a SVG file .
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protected void transcode ( File file , Transcoder transcoder ) throws IOException , TranscoderException { // Disable validation, performance is more important here (thumbnails!) transcoder . addTranscodingHint ( XMLAbstractTranscoder . KEY_XML_PARSER_VALIDATING , Boolean . FALSE ) ; SVGDocument doc = cloneDocument ( ) ; TranscoderInput input = new TranscoderInput ( doc ) ; OutputStream out = new BufferedOutputStream ( new FileOutputStream ( file ) ) ; TranscoderOutput output = new TranscoderOutput ( out ) ; transcoder . transcode ( input , output ) ; out . flush ( ) ; out . close ( ) ; }
Transcode a document into a file using the given transcoder .
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protected SVGDocument cloneDocument ( ) { return ( SVGDocument ) new CloneInlineImages ( ) { @ Override public Node cloneNode ( Document doc , Node eold ) { // Skip elements with noexport attribute set if ( eold instanceof Element ) { Element eeold = ( Element ) eold ; String vis = eeold . getAttribute ( NO_EXPORT_ATTRIBUTE ) ; if ( vis != null && vis . length ( ) > 0 ) { return null ; } } return super . cloneNode ( doc , eold ) ; } } . cloneDocument ( getDomImpl ( ) , document ) ; }
Clone the SVGPlot document for transcoding .
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public void saveAsPDF ( File file ) throws IOException , TranscoderException , ClassNotFoundException { try { Object t = Class . forName ( "org.apache.fop.svg.PDFTranscoder" ) . newInstance ( ) ; transcode ( file , ( Transcoder ) t ) ; } catch ( InstantiationException | IllegalAccessException e ) { throw new ClassNotFoundException ( "Could not instantiate PDF transcoder - is Apache FOP installed?" , e ) ; } }
Transcode file to PDF .
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public void saveAsPNG ( File file , int width , int height ) throws IOException , TranscoderException { PNGTranscoder t = new PNGTranscoder ( ) ; t . addTranscodingHint ( PNGTranscoder . KEY_WIDTH , new Float ( width ) ) ; t . addTranscodingHint ( PNGTranscoder . KEY_HEIGHT , new Float ( height ) ) ; transcode ( file , t ) ; }
Transcode file to PNG .
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public void saveAsANY ( File file , int width , int height , float quality ) throws IOException , TranscoderException , TransformerFactoryConfigurationError , TransformerException , ClassNotFoundException { String extension = FileUtil . getFilenameExtension ( file ) ; if ( "svg" . equals ( extension ) ) { saveAsSVG ( file ) ; } else if ( "pdf" . equals ( extension ) ) { saveAsPDF ( file ) ; } else if ( "ps" . equals ( extension ) ) { saveAsPS ( file ) ; } else if ( "eps" . equals ( extension ) ) { saveAsEPS ( file ) ; } else if ( "png" . equals ( extension ) ) { saveAsPNG ( file , width , height ) ; } else if ( "jpg" . equals ( extension ) || "jpeg" . equals ( extension ) ) { saveAsJPEG ( file , width , height , quality ) ; } else { throw new IOException ( "Unknown file extension: " + extension ) ; } }
Save a file trying to auto - guess the file type .
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public BufferedImage makeAWTImage ( int width , int height ) throws TranscoderException { ThumbnailTranscoder t = new ThumbnailTranscoder ( ) ; t . addTranscodingHint ( PNGTranscoder . KEY_WIDTH , new Float ( width ) ) ; t . addTranscodingHint ( PNGTranscoder . KEY_HEIGHT , new Float ( height ) ) ; // Don't clone. Assume this is used safely. TranscoderInput input = new TranscoderInput ( document ) ; t . transcode ( input , null ) ; return t . getLastImage ( ) ; }
Convert the SVG to a thumbnail image .
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public void dumpDebugFile ( ) { try { File f = File . createTempFile ( "elki-debug" , ".svg" ) ; f . deleteOnExit ( ) ; this . saveAsSVG ( f ) ; LoggingUtil . warning ( "Saved debug file to: " + f . getAbsolutePath ( ) ) ; } catch ( Throwable err ) { // Ignore. } }
Dump the SVG plot to a debug file .
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public void putIdElement ( String id , Element obj ) { objWithId . put ( id , new WeakReference <> ( obj ) ) ; }
Add an object id .
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public Element getIdElement ( String id ) { WeakReference < Element > ref = objWithId . get ( id ) ; return ( ref != null ) ? ref . get ( ) : null ; }
Get an element by its id .
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protected ScoreResult computeScore ( DBIDs ids , DBIDs outlierIds , OutlierResult or ) throws IllegalStateException { if ( scaling instanceof OutlierScaling ) { OutlierScaling oscaling = ( OutlierScaling ) scaling ; oscaling . prepare ( or ) ; } final ScalingFunction innerScaling ; // If we have useful (finite) min/max, use these for binning. double min = scaling . getMin ( ) ; double max = scaling . getMax ( ) ; if ( Double . isInfinite ( min ) || Double . isNaN ( min ) || Double . isInfinite ( max ) || Double . isNaN ( max ) ) { innerScaling = new IdentityScaling ( ) ; // TODO: does the outlier score give us this guarantee? LOG . warning ( "JudgeOutlierScores expects values between 0.0 and 1.0, but we don't have such a guarantee by the scaling function: min:" + min + " max:" + max ) ; } else { if ( min == 0.0 && max == 1.0 ) { innerScaling = new IdentityScaling ( ) ; } else { innerScaling = new LinearScaling ( 1.0 / ( max - min ) , - min ) ; } } double posscore = 0.0 ; double negscore = 0.0 ; // fill histogram with values of each object for ( DBIDIter iter = ids . iter ( ) ; iter . valid ( ) ; iter . advance ( ) ) { double result = or . getScores ( ) . doubleValue ( iter ) ; result = innerScaling . getScaled ( scaling . getScaled ( result ) ) ; posscore += ( 1.0 - result ) ; } for ( DBIDIter iter = outlierIds . iter ( ) ; iter . valid ( ) ; iter . advance ( ) ) { double result = or . getScores ( ) . doubleValue ( iter ) ; result = innerScaling . getScaled ( scaling . getScaled ( result ) ) ; negscore += result ; } posscore /= ids . size ( ) ; negscore /= outlierIds . size ( ) ; LOG . verbose ( "Scores: " + posscore + " " + negscore ) ; ArrayList < double [ ] > s = new ArrayList <> ( 1 ) ; s . add ( new double [ ] { ( posscore + negscore ) * .5 , posscore , negscore } ) ; return new ScoreResult ( s ) ; }
Evaluate a single outlier score result .
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@ Override protected Cluster < SubspaceModel > runDOC ( Database database , Relation < V > relation , ArrayModifiableDBIDs S , int d , int n , int m , int r , int minClusterSize ) { // Relevant attributes of highest cardinality. long [ ] D = null ; // The seed point for the best dimensions. DBIDVar dV = DBIDUtil . newVar ( ) ; // Inform the user about the progress in the current iteration. FiniteProgress iprogress = LOG . isVerbose ( ) ? new FiniteProgress ( "Iteration progress for current cluster" , m * n , LOG ) : null ; Random random = rnd . getSingleThreadedRandom ( ) ; DBIDArrayIter iter = S . iter ( ) ; outer : for ( int i = 0 ; i < n ; ++ i ) { // Pick a random seed point. iter . seek ( random . nextInt ( S . size ( ) ) ) ; for ( int j = 0 ; j < m ; ++ j ) { // Choose a set of random points. DBIDs randomSet = DBIDUtil . randomSample ( S , r , random ) ; // Initialize cluster info. long [ ] nD = BitsUtil . zero ( d ) ; // Test each dimension. for ( int k = 0 ; k < d ; ++ k ) { if ( dimensionIsRelevant ( k , relation , randomSet ) ) { BitsUtil . setI ( nD , k ) ; } } if ( D == null || BitsUtil . cardinality ( nD ) > BitsUtil . cardinality ( D ) ) { D = nD ; dV . set ( iter ) ; if ( BitsUtil . cardinality ( D ) >= d_zero ) { if ( iprogress != null ) { iprogress . setProcessed ( iprogress . getTotal ( ) , LOG ) ; } break outer ; } } LOG . incrementProcessed ( iprogress ) ; } } LOG . ensureCompleted ( iprogress ) ; // If no relevant dimensions were found, skip it. if ( D == null || BitsUtil . cardinality ( D ) == 0 ) { return null ; } // Get all points in the box. DBIDs C = findNeighbors ( dV , D , S , relation ) ; // If we have a non-empty cluster, return it. return ( C . size ( ) >= minClusterSize ) ? makeCluster ( relation , C , D ) : null ; }
Performs a single run of FastDOC finding a single cluster .
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public static SVGPath drawDelaunay ( Projection2D proj , List < SweepHullDelaunay2D . Triangle > delaunay , List < double [ ] > means ) { final SVGPath path = new SVGPath ( ) ; for ( SweepHullDelaunay2D . Triangle del : delaunay ) { path . moveTo ( proj . fastProjectDataToRenderSpace ( means . get ( del . a ) ) ) ; path . drawTo ( proj . fastProjectDataToRenderSpace ( means . get ( del . b ) ) ) ; path . drawTo ( proj . fastProjectDataToRenderSpace ( means . get ( del . c ) ) ) ; path . close ( ) ; } return path ; }
Draw the Delaunay triangulation .
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public static SVGPath drawFakeVoronoi ( Projection2D proj , List < double [ ] > means ) { CanvasSize viewport = proj . estimateViewport ( ) ; final SVGPath path = new SVGPath ( ) ; // Difference final double [ ] dirv = VMath . minus ( means . get ( 1 ) , means . get ( 0 ) ) ; VMath . rotate90Equals ( dirv ) ; double [ ] dir = proj . fastProjectRelativeDataToRenderSpace ( dirv ) ; // Mean final double [ ] mean = VMath . plus ( means . get ( 0 ) , means . get ( 1 ) ) ; VMath . timesEquals ( mean , 0.5 ) ; double [ ] projmean = proj . fastProjectDataToRenderSpace ( mean ) ; double factor = viewport . continueToMargin ( projmean , dir ) ; path . moveTo ( projmean [ 0 ] + factor * dir [ 0 ] , projmean [ 1 ] + factor * dir [ 1 ] ) ; // Inverse direction: dir [ 0 ] *= - 1 ; dir [ 1 ] *= - 1 ; factor = viewport . continueToMargin ( projmean , dir ) ; path . drawTo ( projmean [ 0 ] + factor * dir [ 0 ] , projmean [ 1 ] + factor * dir [ 1 ] ) ; return path ; }
Fake Voronoi diagram . For two means only
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public void select ( Segment segment , boolean addToSelection ) { // abort if segment represents pairs inNone. Would select all segments... if ( segment . isNone ( ) ) { return ; } if ( ! addToSelection ) { deselectAllSegments ( ) ; } // get selected segments if ( segment . isUnpaired ( ) ) { // check if all segments are selected if ( addToSelection ) { boolean allSegmentsSelected = true ; for ( Segment other : segments . getPairedSegments ( segment ) ) { if ( ! isSelected ( other ) ) { allSegmentsSelected = false ; break ; } } // if all are selected, deselect all if ( allSegmentsSelected ) { deselectSegment ( segment ) ; return ; } } if ( isSelected ( segment ) ) { deselectSegment ( segment ) ; } else { selectSegment ( segment ) ; } } else { // an object segment was selected if ( isSelected ( segment ) ) { deselectSegment ( segment ) ; } else { selectSegment ( segment ) ; } } }
Adds or removes the given segment to the selection . Depending on the clustering and cluster selected and the addToSelection option given the current selection will be modified . This method is called by clicking on a segment and ring and the CTRL - button status .
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protected void deselectSegment ( Segment segment ) { if ( segment . isUnpaired ( ) ) { ArrayList < Segment > remove = new ArrayList <> ( ) ; // remove all object segments associated with unpaired segment from // selection list for ( Entry < Segment , Segment > entry : indirectSelections . entrySet ( ) ) { if ( entry . getValue ( ) == segment ) { remove . add ( entry . getKey ( ) ) ; } } for ( Segment other : remove ) { indirectSelections . remove ( other ) ; deselectSegment ( other ) ; } } else { // check if deselected object Segment has a unpaired segment highlighted Segment unpaired = indirectSelections . get ( segment ) ; if ( unpaired != null ) { // remove highlight deselectSegment ( unpaired ) ; } if ( selectedSegments . remove ( segment ) && segment . getDBIDs ( ) != null ) { unselectedObjects . addDBIDs ( segment . getDBIDs ( ) ) ; } } }
Deselect a segment
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protected void selectSegment ( Segment segment ) { if ( segment . isUnpaired ( ) ) { // remember selected unpaired segment for ( Segment other : segments . getPairedSegments ( segment ) ) { indirectSelections . put ( other , segment ) ; selectSegment ( other ) ; } } else { if ( ! selectedSegments . contains ( segment ) ) { selectedSegments . add ( segment ) ; if ( segment . getDBIDs ( ) != null ) { unselectedObjects . removeDBIDs ( segment . getDBIDs ( ) ) ; } } } }
Select a segment
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private boolean checkSupertypes ( Class < ? > cls ) { for ( Class < ? > c : knownParameterizables ) { if ( c . isAssignableFrom ( cls ) ) { return true ; } } return false ; }
Check all supertypes of a class .
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private State checkV3Parameterization ( Class < ? > cls , State state ) throws NoClassDefFoundError { // check for a V3 Parameterizer class for ( Class < ? > inner : cls . getDeclaredClasses ( ) ) { if ( AbstractParameterizer . class . isAssignableFrom ( inner ) ) { try { Class < ? extends AbstractParameterizer > pcls = inner . asSubclass ( AbstractParameterizer . class ) ; pcls . newInstance ( ) ; if ( checkParameterizer ( cls , pcls ) ) { if ( state == State . INSTANTIABLE ) { LOG . warning ( "More than one parameterization method in class " + cls . getName ( ) ) ; } state = State . INSTANTIABLE ; } } catch ( Exception | Error e ) { LOG . verbose ( "Could not run Parameterizer: " + inner . getName ( ) + ": " + e . getMessage ( ) ) ; // continue. Probably non-public } } } return state ; }
Check for a V3 constructor .
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private State checkDefaultConstructor ( Class < ? > cls , State state ) throws NoClassDefFoundError { try { cls . getConstructor ( ) ; return State . DEFAULT_INSTANTIABLE ; } catch ( Exception e ) { // do nothing. } return state ; }
Check for a default constructor .
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public static double logpdf ( double x , double k , double theta , double shift ) { x = ( x - shift ) ; if ( x <= 0. ) { return Double . NEGATIVE_INFINITY ; } final double log1px = FastMath . log1p ( x ) ; return k * FastMath . log ( theta ) - GammaDistribution . logGamma ( k ) - ( theta + 1. ) * log1px + ( k - 1 ) * FastMath . log ( log1px ) ; }
LogGamma distribution logPDF
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protected void plotGray ( SVGPlot plot , Element parent , double x , double y , double size ) { Element marker = plot . svgCircle ( x , y , size * .5 ) ; SVGUtil . setStyle ( marker , SVGConstants . CSS_FILL_PROPERTY + ":" + greycolor ) ; parent . appendChild ( marker ) ; }
Plot a replacement marker when an object is to be plotted as disabled usually gray .
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protected void plotUncolored ( SVGPlot plot , Element parent , double x , double y , double size ) { Element marker = plot . svgCircle ( x , y , size * .5 ) ; SVGUtil . setStyle ( marker , SVGConstants . CSS_FILL_PROPERTY + ":" + dotcolor ) ; parent . appendChild ( marker ) ; }
Plot a replacement marker when no color is set ; usually black
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public int truePositives ( ) { int tp = 0 ; for ( int i = 0 ; i < confusion . length ; i ++ ) { tp += truePositives ( i ) ; } return tp ; }
The number of correctly classified instances .
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public int trueNegatives ( int classindex ) { int tn = 0 ; for ( int i = 0 ; i < confusion . length ; i ++ ) { for ( int j = 0 ; j < confusion [ i ] . length ; j ++ ) { if ( i != classindex && j != classindex ) { tn += confusion [ i ] [ j ] ; } } } return tn ; }
The number of true negatives of the specified class .
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public int falsePositives ( int classindex ) { int fp = 0 ; for ( int i = 0 ; i < confusion [ classindex ] . length ; i ++ ) { if ( i != classindex ) { fp += confusion [ classindex ] [ i ] ; } } return fp ; }
The false positives for the specified class .
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public int falseNegatives ( int classindex ) { int fn = 0 ; for ( int i = 0 ; i < confusion . length ; i ++ ) { if ( i != classindex ) { fn += confusion [ i ] [ classindex ] ; } } return fn ; }
The false negatives for the specified class .
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